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Record W2956268941 · doi:10.1130/b32036.1

Global warming leads to Early Triassic nutrient stress across northern Pangea

2019· article· en· W2956268941 on OpenAlexaff
Stephen E. Grasby, Jochen Knies, Benoı̂t Beauchamp, David P.G. Bond, Paul B. Wignall, Yadong Sun

Bibliographic record

VenueGeological Society of America Bulletin · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsUniversity of CalgaryGeological Survey of Canada
FundersNatural Environment Research CouncilSight Research UK
KeywordsGeologyEarly TriassicExtinction eventPermianOceanographyPaleontologyArcticProductivityDominance (genetics)NutrientGlobal warmingClimate changeEcologyPopulation

Abstract

fetched live from OpenAlex

Abstract The largest extinction in Earth history, in the latest Permian, was followed throughout most of the Early Triassic by a prolonged period of ecologic recovery. What factors delayed biotic recovery are still under debate and partly revolve around impacts of global warming on primary marine productivity. We examined N isotope records from the Festningen section on Spitsbergen, Arctic Norway, to examine changes in nutrient availability through the Early to Middle Triassic along the northern margin of Pangea. Our results show progressive decline in N availability throughout the Griesbachian, leading to severe nutrient limitations through the remainder of the Early Triassic, until returning to a highly productive continental margin in Middle Triassic time. These results are consistent with other studies from northern and western Pangea and thus show regional nutrient limitations occurred in what should have been the main zone of marine primary productivity. Such nutrient limitation likely stressed primary production and consequently contributed to prolonged marine recovery. We suggest this was driven by high ocean temperatures depressing the marine nutricline.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.234
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations59
Published2019
Admission routes1
Has abstractyes

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